Gender: Male 
Age: 35-44
Nationality: United Kingdom
Ethnicity: White / Caucasian 
Marital status: Married 
First language: English 
Religion: Christian 
Education: Graduate degree
Mother's education: High school or below
Father's education: Graduate degree
Self-assessed socioeconomic status (1-10): 7
Home: Own 
Employment: Employed full time
Occupation: Professionals or highly skilled workers (those who increase the existing stock of knowledge or teach about it in a systematic manner, e.g. engineer, doctor, teacher, lawyer)
Mother's occupation: Homemaker
Father's occupation: Service and sales workers (whose who provide personal and protective services related to travel, housekeeping, catering, personal care, protection against fire and unlawful acts)
Hobbies: Go to gigs;Go to museums and galleries;Watch sports 
Devices used daily: Smartphone;Tablet;Laptop 
NLP tools known: Grammar checker (i.e., correcting grammar mistakes);Spell checker (i.e., correcting misspellings);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Sentiment analysis (e.g. automatically detect if user reviews are positive or negative)Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Text-to-Speech (i.e, computers being able to read out loud written language);Other (specify);Dialog technology (e.g. chatbots that communicate with you such as chatGPT) 
NLP tools used: E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Grammar checker (i.e., correcting grammar mistakes);Spell checker (i.e., correcting misspellings);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Text-to-Speech (i.e, computers being able to read out loud written language);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Other (specify) 
NLP tools they would find useful: E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);Sentiment analysis (e.g. automatically detect if user reviews are positive or negative)Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Text-to-Speech (i.e, computers being able to read out loud written language);Speech-to-Text (i.e. a computer transcribing your spoken language to written language) 
Frequency of LLM use: Nearly every day
LLMs used: ChatGPT;Google Gemini 
LLM use cases: Coding;Translation;Brainstorming;Writing (e.g., composing emails);Paraphrasing or finding synonyms;Analyze data;Solving math or logical problems;Proofreading/Editing (e.g., grammar, spelling, structure improvements) 
Contexts of LLM use: Work;Entertainment 
Prompt 1: Check text below 
Prompt 2: Write email about sales
Prompt 3: Check data for errors
Prompt 4: What is an effective negotiation technique 
Prompt 5: Where is Hq of certain company 
Prompt 6: Do a SWOT on competitor 
Prompt 7: Summarise CV
Prompt 8: Tell me about the history of medical device
Prompt 9: Provide a list of phrases for saying no politely 
Prompt 10: How to run a sub 20min 5k
Comments: No